reka-mcp 0.1.1

Official Reka MCP server for video understanding

Reka Vision MCP Server

mcp-name: ai.reka/mcp

MCP server that lets AI agents upload, index, search, and analyze videos through the Reka Vision API. Agents can search across videos using natural language, ask questions about video content with visual analysis, and read processed data like transcripts, captions, scenes, and detected objects.

Quick Start

# Run the published stdio server
uvx reka-mcp

# Run with an API key
REKA_VISION_API_KEY="your-api-key" uvx reka-mcp

For MCP clients that need explicit command configuration:

{
  "command": "uvx",
  "args": ["reka-mcp"],
  "env": {
    "REKA_VISION_API_KEY": "your-api-key-here"
  }
}

For local development:

uv sync
uv run pre-commit install
REKA_VISION_API_KEY="test-key" uv run reka-mcp

The stdio transport is the default. Use HTTP only when hosting the MCP server:

REKA_MCP_TRANSPORT=http REKA_MCP_AUTH_TOKEN="your-secret" uv run python -m reka_mcp

Claude Desktop Setup

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "reka-vision": {
      "command": "uvx",
      "args": ["reka-mcp"],
      "env": {
        "REKA_VISION_API_KEY": "your-api-key-here"
      }
    }
  }
}

Cursor Setup

Add to .cursor/mcp.json:

{
  "mcpServers": {
    "reka-vision": {
      "command": "uvx",
      "args": ["reka-mcp"],
      "env": {
        "REKA_VISION_API_KEY": "your-api-key-here"
      }
    }
  }
}

Claude Code Setup

claude mcp add reka-vision -- uvx reka-mcp

Set the API key in your environment or .claude/settings.json.

Available Tools

Tool	Description
upload_video	Upload a video from a URL or local file path
list_videos	List videos in your account or a group
get_video	Get video details, metadata, and feature status
delete_video	Permanently delete a video and all indexed data
create_group	Create a new video group
list_groups	List all video groups
delete_group	Delete a video group
index_video	Index a video for search/QA/analysis (2-10 min)
search_videos	Semantic search across indexed videos
ask_video	Ask questions about video content with visual analysis
get_transcript	Get transcript as text, segments, or words
get_captions	Get AI-generated visual descriptions
get_scenes	Get detected scene boundaries
get_objects	Get detected objects with bounding boxes
get_feature_catalog	List available features and dependencies
summarize_video	Compact overview of video content and status

Environment Variables

Variable	Default	Description
REKA_VISION_API_KEY	(required)	API key from https://platform.reka.ai
REKA_VISION_API_URL	https://vision-api.reka.ai	API base URL
REKA_MCP_INDEX_TIMEOUT	600	Max seconds to wait for indexing
REKA_MCP_POLL_INTERVAL	5	Seconds between index status polls
REKA_MCP_TRANSPORT	stdio	Transport: stdio or http
REKA_MCP_HTTP_PORT	8080	Port for HTTP transport
REKA_MCP_AUTH_TOKEN	(none)	Bearer token for HTTP transport auth. Clients must send Authorization: Bearer <token>. Recommended when using HTTP transport.

Release Checks

cd /path/to/reka-mcp
uv build
uv run twine check dist/*
# Publish only after explicit approval:
uv run twine upload dist/*

Example Workflows

Search and Visual Q&A

Agent: search_videos(query="revenue chart")
→ [{video_id: "v1", start: 30.0, end: 35.0, score: 0.95}]

Agent: ask_video(question="What numbers are on the chart?",
                 video_id="v1", start=30.0, end=35.0)
→ {answer: "Q3 revenue of $4.2M, up 32%...", conversation_id: "c1"}

Agent: ask_video(question="What's the percentage change?",
                 conversation_id="c1")
→ {answer: "Revenue increased by 32% quarter-over-quarter..."}

Cross-Video Comparison

Agent: search_videos(query="quarterly revenue")
→ [{video_id: "v1", start: 30.0, ...}, {video_id: "v2", start: 120.0, ...}]

Agent: ask_video(question="How do the revenue figures compare?",
                 videos=[
                   {video_id: "v1", start: 30.0, end: 35.0},
                   {video_id: "v2", start: 120.0, end: 125.0}
                 ])
→ {answer: "Video 1 shows Q3 at $4.2M while Video 2 shows Q4 at $5.1M..."}

Video Summary and Transcript Extraction

Agent: summarize_video(video_id="v1")
→ {name: "Lecture 3", duration_seconds: 3600, features: {...},
   scene_count: 42, transcript_preview: "Welcome to today's lecture..."}

Agent: get_transcript(video_id="v1", format="segments", start=0, end=60)
→ {data: [{start: 0.0, end: 5.2, text: "Welcome..."}, ...],
   total_count: 12, truncated: false}